SoroushVahidi/scidocs huggingface dataset:
MPTLsim: a cycle-accurate, full-system simulator for x86-64 multicore architectures with coherent caches
(2009)

The introduction of multicore microprocessors in the recent years has made it imperative to use cycleaccurate and full-system simulators in the architecture research community. We introduce MPTLsim - a multicore simulator for the X86 ISA that meets this need. MPTLsim is a uop-accurate, cycle-accurate, full-system simulator for multicore designs based on the X86-64 ISA. MPTLsim extends PTLsim, a publicly available single core simulator, with a host of additional features to support hyperthreading within a core and multiple cores, with detailed models for caches, on-chip interconnections and the memory data flow. MPTLsim incorporates detailed simulation models for cache controllers, interconnections and has built-in implementations of a number of cache coherency protocols.
Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams
(2016)

The name entity disambiguation task aims to partition the records of multiple real-life persons so that each partition contains records pertaining to a unique person. Most of the existing solutions for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. However, more realistic settings require that the name disambiguation task be performed in an online fashion, in addition to, being able to identify records of new ambiguous entities having no preexisting records. In this work, we propose a Bayesian non-exhaustive classification framework for solving online name disambiguation task. Our proposed method uses a Dirichlet process prior with a Normal x Normal x Inverse Wishart data model which enables identification of new ambiguous entities who have no records in the training data. For online classification, we use one sweep Gibbs sampler which is very efficient and effective. As a case study we consider bibliographic data in a temporal stream format and disambiguate authors by partitioning their papers into homogeneous groups. Our experimental results demonstrate that the proposed method is better than existing methods for performing online name disambiguation task.
Further improvement in reducing superficial contamination in NIRS using double short separation measurements
(2014)

Near-Infrared Spectroscopy (NIRS) allows the recovery of the evoked hemodynamic response to brain activation. In adult human populations, the NIRS signal is strongly contaminated by systemic interference occurring in the superficial layers of the head. An approach to overcome this difficulty is to use additional NIRS measurements with short optode separations to measure the systemic hemodynamic fluctuations occurring in the superficial layers. These measurements can then be used as regressors in the post-experiment analysis to remove the systemic contamination and isolate the brain signal. In our previous work, we showed that the systemic interference measured in NIRS is heterogeneous across the surface of the scalp. As a consequence, the short separation measurement used in the regression procedure must be located close to the standard NIRS channel from which the evoked hemodynamic response of the brain is to be recovered. Here, we demonstrate that using two short separation measurements, one at the source optode and one at the detector optode, further increases the performance of the short separation regression method compared to using a single short separation measurement. While a single short separation channel produces an average reduction in noise of 33% for HbO, using a short separation channel at both source and detector reduces noise by 59% compared to the standard method using a general linear model (GLM) without short separation. For HbR, noise reduction of 3% is achieved using a single short separation and this number goes to 47% when two short separations are used. Our work emphasizes the importance of integrating short separation measurements both at the source and at the detector optode of the standard channels from which the hemodynamic response is to be recovered. While the implementation of short separation sources presents some difficulties experimentally, the improvement in noise reduction is significant enough to justify the practical challenges.
Magnetic integration of LCC compensated resonant converter for inductive power transfer applications
(2014)

The aim of this paper is to present a novel magnetic integrated LCC series-parallel compensation topology for the design of both the primary and pickup pads in inductive power transfer (IPT) applications. A more compact structure can be realized by integrating the inductors of the compensation circuit into the coupled power-transmitting coils. The impact of the extra coupling between the compensated coils (inductors) and the power-transferring coils is modeled and analyzed. The basic characteristics of the proposed topology are studied based on the first harmonic approximation (FHA). High-order harmonics are taken into account to derive an analytical solution for the current at the switching instant, which is helpful for the design of soft-switching operation. An IPT system with up to 5.6kW output power for electric vehicles (EV) charger has been built to verify the validity of the proposed magnetic integrated compensation topology. A peak efficiency of 95.36% from DC power source to the battery load is achieved at rated operation condition.
Fine-to-Coarse Global Registration of RGB-D Scans
(2017)

RGB-D scanning of indoor environments is important for many applications, including real estate, interior design, and virtual reality. However, it is still challenging to register RGB-D images from a hand-held camera over a long video sequence into a globally consistent 3D model. Current methods often can lose tracking or drift and thus fail to reconstruct salient structures in large environments (e.g., parallel walls in different rooms). To address this problem, we propose a fine-to-coarse global registration algorithm that leverages robust registrations at finer scales to seed detection and enforcement of new correspondence and structural constraints at coarser scales. To test global registration algorithms, we provide a benchmark with 10,401 manually-clicked point correspondences in 25 scenes from the SUN3D dataset. During experiments with this benchmark, we find that our fine-to-coarse algorithm registers long RGB-D sequences better than previous methods.
Automatically Quantifying Radiographic Knee Osteoarthritis Severity Final Report-CS 229-Machine Learning
(2016)

In this paper, we implement machine learning algorithms to automatically quantify knee osteoarthritis severity from X-ray images according to the Kellgren & Lawrence (KL) grades. We implement and evaluate the performance of various machine learning models like transfer learning, support vector machines and fully connected neural networks based on their classification accuracy. We also implement the task of automatically extracting the knee-joint region from the X-ray images and quantifying their severity by training a faster region convolutional neural network (R-CNN).
Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
(2018)

Perception and expression of emotion are key factors to the success of dialogue systems or conversational agents. However, this problem has not been studied in large-scale conversation generation so far. In this paper, we propose Emotional Chatting Machine (ECM) that can generate appropriate responses not only in content (relevant and grammatical) but also in emotion (emotionally consistent). To the best of our knowledge, this is the first work that addresses the emotion factor in large-scale conversation generation. ECM addresses the factor using three new mechanisms that respectively (1) models the high-level abstraction of emotion expressions by embedding emotion categories, (2) captures the change of implicit internal emotion states, and (3) uses explicit emotion expressions with an external emotion vocabulary. Experiments show that the proposed model can generate responses appropriate not only in content but also in emotion.
Information Retrieval with Verbose Queries
(2015)

Recently, the focus of many novel search applications shifted from short keyword queries to verbose natural language queries. Examples include question answering systems and dialogue systems, voice search on mobile devices and entity search engines like Facebook's Graph Search or Google's Knowledge Graph. However the performance of textbook information retrieval techniques for such verbose queries is not as good as that for their shorter counterparts. Thus, effective handling of verbose queries has become a critical factor for adoption of information retrieval techniques in this new breed of search applications. Over the past decade, the information retrieval community has deeply explored the problem of transforming natural language verbose queries using operations like reduction, weighting, expansion, reformulation and segmentation into more effective structural representations. However, thus far, there was not a coherent and organized tutorial on this topic. In this tutorial, we aim to put together various research pieces of the puzzle, provide a comprehensive and structured overview of various proposed methods, and also list various application scenarios where effective verbose query processing can make a significant difference.
Biologically inspired protection of deep networks from adversarial attacks
(2017)

Inspired by biophysical principles underlying nonlinear dendritic computation in neural circuits, we develop a scheme to train deep neural networks to make them robust to adversarial attacks. Our scheme generates highly nonlinear, saturated neural networks that achieve state of the art performance on gradient based adversarial examples on MNIST, despite never being exposed to adversarially chosen examples during training. Moreover, these networks exhibit unprecedented robustness to targeted, iterative schemes for generating adversarial examples, including second-order methods. We further identify principles governing how these networks achieve their robustness, drawing on methods from information geometry. We find these networks progressively create highly flat and compressed internal representations that are sensitive to very few input dimensions, while still solving the task. Moreover, they employ highly kurtotic weight distributions, also found in the brain, and we demonstrate how such kurtosis can protect even linear classifiers from adversarial attack.
A fully balanced pseudo-differential OTA with common-mode feedforward and inherent common-mode feedback detector
(2003)

A pseudo-differential fully balanced fully symmetric CMOS operational transconductance amplifier (OTA) architecture with inherent common-mode detection is proposed. Through judicious arrangement, the common-mode feedback circuit can be economically implemented. The OTA achieves a third harmonic distortion of 43 dB for 900 mVpp at 30 MHz. The OTA, fabricated in 0.5m CMOS process, is used to design a 100-MHz fourth-order linear phase filter. The measured filter’s group delay ripple is 3% for frequencies up to 100 MHz, and the measured dynamic range is 45 dB for a total harmonic distortion of 46 dB. The filter consumes 42.9 mW per complex pole pair while operating from a 1.65-V power supply.
Emergence of Fatal PRRSV Variants: Unparalleled Outbreaks of Atypical PRRS in China and Molecular Dissection of the Unique Hallmark
(2007)

Porcine reproductive and respiratory syndrome (PRRS) is a severe viral disease in pigs, causing great economic losses worldwide each year. The causative agent of the disease, PRRS virus (PRRSV), is a member of the family Arteriviridae. Here we report our investigation of the unparalleled large-scale outbreaks of an originally unknown, but so-called "high fever" disease in China in 2006 with the essence of PRRS, which spread to more than 10 provinces (autonomous cities or regions) and affected over 2,000,000 pigs with about 400,000 fatal cases. Different from the typical PRRS, numerous adult sows were also infected by the "high fever" disease. This atypical PRRS pandemic was initially identified as a hog cholera-like disease manifesting neurological symptoms (e.g., shivering), high fever (40-42 degrees C), erythematous blanching rash, etc. Autopsies combined with immunological analyses clearly showed that multiple organs were infected by highly pathogenic PRRSVs with severe pathological changes observed. Whole-genome analysis of the isolated viruses revealed that these PRRSV isolates are grouped into Type II and are highly homologous to HB-1, a Chinese strain of PRRSV (96.5% nucleotide identity). More importantly, we observed a unique molecular hallmark in these viral isolates, namely a discontinuous deletion of 30 amino acids in nonstructural protein 2 (NSP2). Taken together, this is the first comprehensive report documenting the 2006 epidemic of atypical PRRS outbreak in China and identifying the 30 amino-acid deletion in NSP2, a novel determining factor for virulence which may be implicated in the high pathogenicity of PRRSV, and will stimulate further study by using the infectious cDNA clone technique.
Destination bonding : Hybrid cognition using Instagram
(2015)

Article history: Received August 28, 2015 Received in revised format November 28, 2015 Accepted December 1, 2015 Available online December 1, 2015 Empirical research has identified the phenomenon of destination bonding as a result of summated physical and emotional values associated with the destination. Physical values, namely natural landscape & other physical settings and emotional values, namely the enculturation processes, have a significant role to play in portraying visitors’ cognitive framework for destination preference. The physical values seemed to be the stimulator for bonding that embodies action or behavior tendencies in imagery. The emotional values were the conditions that lead to affective bonding and are reflected in attitudes for a place which were evident in text narratives. Social networking on virtual platforms offers the scope for hybrid cognitive expression using imagery and text to the visitors. Instagram has emerged as an application-window to capture these hybrid cognitions of visitors. This study focuses on assessing the relationship between hybrid cognition of visitors expressed via Instagram and their bond with the destination. Further to this, the study attempts to examine the impact of hybrid cognition of visitors on the behavioral pattern of prospective visitors to the destination. The study revealed that sharing of visual imageries and related text by the visitors is an expression of the physico-emotional bonding with the destination. It was further established that hybrid cognition strongly asserts destination bonding and has been also found to have moderating impact on the link between destination bonding and electronic-word-of-mouth. © 2016 Growing Science Ltd. All rights reserved.
Hand grasp and motion for intent expression in mid-air virtual pottery
(2015)

We describe the design and evaluation of a geometric interaction technique for bare-hand mid-air virtual pottery. We model the shaping of a pot as a gradual and progressive convergence of the potprofile to the shape of the user’s hand represented as a point-cloud (PCL). Our pottery-inspired application served as a platform for systematically revealing how users use their hands to express the intent of deformation during a pot shaping process. Through our approach, we address two specific problems: (a) determining start and end of deformation without explicit clutching and declutching, and (b) identifying user’s intent by characterizing grasp and motion of the hand on the pot. We evaluated our approach’s performance in terms of intent classification, users’ behavior, and users’ perception of controllability. We found that the expressive capability of hand articulation can be effectively harnessed for controllable shaping by organizing the deformation process in broad classes of intended operations such as pulling, pushing and fairing. After minimal practice with the pottery application, users could figure out their own strategy for reaching, grasping and deforming the pot. Further, the use of PCL as mid-air input allows for using common physical objects as tools for pot deformation. Users particularly enjoyed this aspect of our method for shaping pots.
SoftGUESS: Visualization and Exploration of Code Clones in Context
(2007)

We introduce SoftGUESS, a code clone exploration system. SoftGUESS is built on the more general GUESS system which provides users with a mechanism to interactively explore graph structures both through direct manipulation as well as a domain-specific language. We demonstrate SoftGUESS through a number of mini-applications to analyze evolutionary code-clone behavior in software systems. The mini-applications of SoftGUESS represent a novel way of looking at code-clones in the context of many system features. It is our hope that SoftGUESS will form the basis for other analysis tools in the software-engineering domain.
The Diffusion of Innovations in Social Networks ∗
(2015)

This paper determines how different network structures influence the diffusion of innovations. We develop a model of diffusion where: 1. an individual’s decision to adopt a new technology is influenced by his contacts; and 2. contacts can discuss, coordinate, and make adoption decisions together. A measure of connectedness, ‘cohesion’, determines diffusion. A cohesive community is defined as a group in which all members have a high proportion of their contacts within the group. We show a key trade-off: on one hand, a cohesive community can hinder diffusion by blocking the spread of a technology into the group; on the other hand, cohesive communities can be particularly effective at acting collectively to adopt an innovation. We find that for technologies with low externalities (that require few people to adopt before others are willing to adopt), social structures with loose ties, where people are not part of cohesive groups, enable greater diffusion. However, as externalities increase (technologies require more people to adopt before others are willing to adopt), social structures with increasingly cohesive groups enable greater diffusion. Given that societal structure is known to differ systematically along this dimension, our findings point to specialization in technological progress exhibiting these patterns. ∗Bryony Reich, Faculty of Economics, University College London. Email: b.reich@ucl.ac.uk. I would like to thank Alberto Alesina, Antonio Cabrales, Sanjeev Goyal, and Jorgen Weibull for their invaluable guidance and support. I benefited greatly from conversations with and comments of Marco Bassetto, Lars Nesheim and Imran Rasul. I am grateful to Jonathan Newton for numerous interactions at all stages of this project. For helpful comments I would also like to thank Lucie Gadenne, Terri Kneeland, and Sueyhun Kwon, as well as seminar participants at Alicante, Cambridge, INET Contagion Conference, Oxford, PET Luxembourg, and UCL. I gratefully acknowledge financial support from the UK Economic and Social Research Council (grant number ES/K001396/1).
SLANGZY: a fuzzy logic-based algorithm for English slang meaning selection
(2018)

The text present on online forums and social media platforms conventionally does not follow a standard sentence structure and uses words that are commonly termed as slang or Internet language. Online text mining involves a surfeit of slang words; however, there is a distinct lack of reliable resources available to find accurate meanings of these words. We aim to bridge this gap by introducing SLANGZY, a fuzzy logic-based algorithm for English slang meaning selection which uses a mathematical factor termed as “slang factor” to judge the accuracy of slang word definitions found in Urban Dictionary, the largest Slang Dictionary on the Internet. This slang factor is used to rank definitions of English slang words retrieved from over 4 million unique words on popular social media platforms such as Twitter, YouTube and Reddit. We investigate the usefulness of SLANGZY over Urban Dictionary to find meanings of slang words in social media text and achieve encouraging results due to recognizing the importance of multiple criteria in the calculation of slang factor in the algorithm over successive experiments. The performance of SLANGZY with optimum weights for each criterion is further assessed using the accuracy, error rate, F-Score as well as a difference factor for English slang word definitions. To further illustrate the results, a web portal is created to display the contents of the Slang Dictionary consisting of definitions ranked according to the calculated slang factors.
A Topic Model for Word Sense Disambiguation
(2007)

We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic posterior inference algorithm for simultaneously disambiguating a corpus and learning the domains in which to consider each word. Using the WORDNET hierarchy, we embed the construction of Abney and Light (1999) in the topic model and show that automatically learned domains improve WSD accuracy compared to alternative contexts.
Probabilistic author-topic models for information discovery
(2004)

We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words for that topic. The words in a multi-author paper are assumed to be the result of a mixture of each authors' topic mixture. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to a large corpus of 160,000 abstracts and 85,000 authors from the well-known CiteSeer digital library, and learn a model with 300 topics. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, significant trends in the computer science literature between 1990 and 2002, parsing of abstracts by topics and authors and detection of unusual papers by specific authors. An online query interface to the model is also discussed that allows interactive exploration of author-topic models for corpora such as CiteSeer.
Finding scientific topics.
(2004)

A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.